Method and apparatus for personalized physiologic parameters

ABSTRACT

Methods and apparatus combine patient measurement data with demographic or physiological data of the patient to determine an output that can be used to diagnose and treat the patient. A customized output can be determined based the demographics of the patient, physiological data of the patient, and data of a population of patients. In another aspect, patient measurement data is used to predict an impending cardiac event, such as acute decompensated heart failure. At least one personalized value is determined for the patient, and a patient event prediction output is generated based at least in part on the personalized value and the measurement data. For example, bioimpedance data may be used to establish a baseline impedance specific to the patient, and the patient event prediction output generated based in part on the relationship of ongoing impedance measurements to the baseline impedance. Multivariate prediction models may enhance prediction accuracy.

This application claims priority from provisional U.S. patentapplication Ser. No. 61/321,040, titled “Method and Apparatus forPersonalized Physiologic Parameters” and filed Apr. 5, 2010, the entiredisclosure of which is hereby incorporated by reference herein for allpurposes.

BACKGROUND OF THE INVENTION

The present invention relates to monitoring and treatment of people andanimals, and more specifically to patient monitoring and treatment ofdisease. Although embodiments make specific reference to monitoringimpedance and electrocardiogram signals with an adherent patch, thesystem methods and device described herein may be applicable to manyapplications in which physiological monitoring is used, for examplewireless physiological monitoring with implantable devices for extendedperiods.

Patients are often treated for diseases and/or conditions associatedwith a compromised status of the patient, for example a compromisedphysiologic status such as heart disease. In some instances a patientmay have suffered a heart attack and require care and/or monitoringafter release from the hospital. While such long term care may be atleast partially effective, many patients are not sufficiently monitoredand eventually succumb to cardiac decompensation, or heart failure. Oneexample of a device that may be used to monitor a patient is the Holtermonitor, or ambulatory electrocardiography device. Although such adevice may be effective in measuring electrocardiography, suchmeasurements alone may not be sufficient to reliably detect and/or avoidan impending cardiac decompensation.

In addition to measuring heart signals with electrocardiograms, knownphysiologic measurements include impedance measurements. For example,transthoracic impedance measurements can be used to measure hydrationand respiration. Although transthoracic measurements can be useful, suchmeasurements may use electrodes that are positioned across the midlineof the patient, and may be somewhat uncomfortable and/or cumbersome forthe patient to wear. Also, known methods of using hydration as a measureof impedance can be subject to error in at least some instances.

Work in relation to embodiments of the present invention suggests thatknown methods and apparatus for long term monitoring of patients may beless than ideal to detect and/or avoid an impending cardiacdecompensation. In at least some instances, cardiac decompensation canbe difficult to detect, for example in the early stages. Althoughbioimpedance and other physiological parameters have been used to assessHF condition and track patient improvement or worsening, at least someof the current methods and apparatus may not predict an impendingpatient event and can result in false positives in at least someinstances.

Therefore, a need exists for improved patient monitoring. Ideally, suchimproved patient monitoring would avoid at least some of theshort-comings of the present methods and devices.

BRIEF SUMMARY OF THE INVENTION

Embodiments of the present invention provide improved methods andapparatus for patient monitoring and treatment. In many embodiments,patient measurement data is combined with demographic or physiologicaldata of the patient to determine an output that can be used to diagnoseand treat the patient. For example a customized output can be determinedbased the demographics of the patient, physiological data of thepatient, and data of a population of patients. The patient populationdata may correspond to data that can influence the data measured fromthe patient such that the output determined with the patient data andthe patient population data is less sensitive to patient characteristicsand can be used more effectively by a treating physician. For example,the measured patient data may correspond to hydration of the patient,such as impedance of the patient, and the output may comprise ahydration indicator based patient the impedance of the patient and datathat can influence the impedance measurement such as patient demographicdata and patient data corresponding to fat. The patient demographic datamay comprise one or more gender, age or race, and the patient datacorresponding to fat may comprise one or more measurements related tofat of the patient such as the height and weight, the body mass index(BMI), or the percent fat based on imaging. The output determined basedon the patient data and the population data may comprise one or more ofa hydration indicator, an adjusted impedance, the hydration indicatorover time, or an event prediction.

In a first aspect, embodiments of the present invention provide anapparatus to monitor a patient. The apparatus comprises at least twoelectrodes coupled to circuitry to measure an impedance of the patient.At least one processor receives the measured impedance and patient data,and the at least one processor is configured to determine an outputbased on the impedance and the patient data.

In many embodiments, the patient data corresponds to a demographic ofthe patient. The patient demographic may correspond to one or more of agender of the patient, an age of the patient, or a race of the patient.

In many embodiments, the patient data corresponds to fat of the patient.The patient data corresponding to fat of the patient comprising one ormore of a percent body fat of the patient, a body mass index, a heightof the patient, a weight of the patient, a caliper measure of the fat ofthe patient, a tape measure test of the patient, a near infraredinteractance, images of the patient to determine fat, dual energy x-rayabsorptiometry (DXA), expansion based on body volume, body averagedensity measurement, a second impedance measurement of the patient, ananthropometeric measurement of body fat, a circumference of the patient,a circumference of a body part, a thickness of a skin fold, or anestimate of body density.

In many embodiments, the output comprises customized patient data basedon patient data corresponding to fat of the patient and at least onepatient demographic comprising one or more of a sex, a race, or an ageof the patient.

In many embodiments, the patient data corresponds to an ejectionfraction of the patient.

In many embodiments, the patient data comprises one or more of bloodpressure, creatinine, blood urea nitrogen (BUN), troponin, ck-mb(creatinine kinase-MB), or a previous event of the patient.

In many embodiments, the output comprises one or more of a patienthydration indicator, an adjusted impedance, a patient populationstatistic adjusted based on the patent data, the hydration indicatorover time, or a patient event prediction output.

In many embodiments, the output may comprise the patient hydrationindicator. The at least one processor can be configured to increase anhydration amount of the patient hydration indicator based on theimpedance measurement and in response to a decrease of fat of thepatient, and the at least one processor can be configured to decreasethe hydration amount of the patient hydration indicator based on theimpedance measurement and in response to an increase of the fat of thepatient.

The hydration indicator may comprise an adjusted impedance of thepatient, and the adjusted impedance may correspond inversely to thehydration of the patient.

In many embodiments, the output comprises the adjusted impedance. The atleast one processor can be configured to determine the adjustedimpedance so as to correspond to an increase of at least about one halfohm per unit increase of body mass index and so as to correspond to adecrease of at least about one half ohm per unit decrease of body massindex. The at least one processor can be configured to determine theadjusted impedance so as to correspond to an increase of at least aboutone ohm per unit increase of body mass index and so as to correspond toa decrease of at least about one ohm per unit decrease of body massindex.

In many embodiments, the output comprises a spot check output based on aspot check impedance measurement of the patient and the patient data.

In many embodiments, the output comprises an acute output based on anacute impedance measurement of the patient and the patient data.

In many embodiments, the output comprises the patient populationstatistic adjusted based on the patient data. The output may comprise afirst marker and a second marker, in which the first marker isdetermined based on data of a population of patients and the patientdata and the second marker is determined based on the data of thepopulation of patients and the patient data. The first marker maycorrespond to dehydration of the patient, and the second markercorresponding to excess body fluid of the patient. The first marker maycomprise an upper adjusted impedance marker corresponding to dehydrationof the patient based on the data of the population of patients, and thesecond marker may comprise a lower adjusted impedance markercorresponding to excessive hydration of the patient based on the data ofthe population of patients.

In many embodiments, the at least one processor is configured todetermine a first hydration marker based on the patient data and asecond hydration marker based on the patient data, the first hydrationmarker indicating dehydration of the patient, the second hydrationmarker indicating excess hydration. The at least one processor can becoupled to a display to show the hydration indicator in relation to thefirst hydration marker and the second hydration marker. The at least oneprocessor can be coupled to a color display and configured to show onthe display the hydration indicator in spatial and color relation to thefirst hydration marker and the second hydration marker. The at least oneprocessor can be configured to show green along a first region of thedisplay extending between the first marker and the second markerconfigured to show red along a second region of the display disposedaway from the first region.

In many embodiments, the output comprises the hydration indicator overtime and wherein the hydration indicator over time comprises a pluralityof hydration indicators based on a plurality of impedance measurementsat least one day apart.

In many embodiments, the output comprises the patient event predictionoutput. The patient event prediction output may comprise a signalcorresponding to prediction of an impending cardiac event of the patientbased on the hydration indicator and the patient data. The eventprediction signal may comprise a signal to predict an impending cardiacdecompensation of the patient based on the adjusted impedance and thepatient data.

In many embodiments, the circuitry is coupled to the at least twoelectrodes to measure the impedance with at least one frequency within arange from about 1 kHz to about 50 kHz. The at least one frequencycomprises a bandwidth of no more than about 5 kHz.

In many embodiments, the apparatus further comprises one or more of acardioverter, an implantable cardioverter-defibrillator (ICD), cardiac adefibrillator, a resynchronization therapy defibrillator (CRT-D) or apacemaker coupled to the at least one processor to treat the patient.

In many embodiments, the at least two electrodes comprise implantableelectrodes. One of the at least two electrodes comprises a housing ofthe apparatus.

In many embodiments, the at least two electrodes comprise gel electrodesto adhere to a skin of the patient.

In another aspect, embodiments of the present invention provide a methodof monitoring a patient. An impedance of the patient is measured, and apatient output is determined based on the measured impedance and apatient data.

In another aspect, embodiments of the present invention provide anapparatus to monitor a patient having a body fluid. The apparatuscomprises means for determining an output based on patient data.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a patient and a monitoring system comprising a patientmeasurement device, according to embodiments of the present invention;

FIG. 1A shows a first patient suitable for monitoring in accordance withembodiments;

FIG. 1B shows a second patient suitable for monitoring in accordancewith embodiments;

FIG. 1C shows a third patient suitable for monitoring in accordance withembodiments;

FIG. 1D shows a fourth patient suitable for monitoring in accordancewith embodiments;

FIGS. 2A and 2A1 show an exploded view and a side cross-sectional view,respectively, of embodiments of the adherent device with a temperaturesensor affixed to the gel cover;

FIG. 2B shows a printed circuit board and electronic components over theadherent patch, as in FIG. 2A;

FIG. 2B1 shows an equivalent circuit that can be used to determineoptimal frequencies for determining patient hydration, according toembodiments of the present invention;

FIG. 2C shows an implantable device suitable for incorporation accordingto embodiments of the present invention;

FIG. 2D shows a viewable display showing an adjusted impedancecorresponding to a personalized fluid level disposed between an upperadjusted marker and a lower adjusted marker;

FIG. 2E shows a plot corresponding to statistical parameters of apatient population that can be combined with patient data to generate adisplay in accordance with embodiments shown in FIG. 2D;

FIG. 2F shows adjusted impedance over time in accordance withembodiments of FIG. 2D and FIG. 2E;

FIG. 3 shows a method of monitoring a patient according to embodimentsof the present invention;

FIG. 4 shows a scatter plot of impedance and body mass index measuredwith a population of approximately 200 patients, in accordance withembodiments;

FIG. 5 illustrates reading impedance data (BioZ) from the adherentdevice in accordance with embodiments;

FIG. 6 illustrates an example method of utilizing the impedanceparameter readings to derive an impedance flag that may be used inpredicting an impending cardiac event;

FIG. 7 illustrates a flowchart of one exemplary embodiment for computinga baseline impedance and an impedance index;

FIG. 8 illustrates a flowchart of one exemplary method of computing abaseline breath parameter and a breath index;

FIG. 9 illustrates a flowchart for computing a ratio index from ongoingmeasurements of the patient's impedance and the patient's predictedimpedance; and

FIG. 10 graphically illustrates event prediction logic according toembodiments.

DETAILED DESCRIPTION OF THE INVENTION

Embodiments as described herein provide apparatus and methods foradjusting physiological parameters measured by a patient measurementdevice so as to determine a personalized value for the assessment of thepatient, for example of a heart failure (HF) condition.

The methods and apparatus may comprised instructions of a computerprogram embodied on a computer readable medium so as to operate inaccordance with algorithm that uses specific and unique parameters froma patient in order to implement a patient specific and personalizedoutput for HF assessment and care. In many embodiments, the output maycomprise a range of normal and abnormal physiological parameters thatare adjusted so as to comprise specific and unique evaluation of thecondition of the patient.

For example, a patient's demographics and characteristics (e.g. race,gender, weight, BMI, etc.) can be used in a formula derived frompopulation data, to yield a patient specific outcomes target. Thesetarget values can be used as initial conditions for treatment or as anideal endpoint for care. The embodiments as described herein may alsoinclude the display of patient data relative to population data; andsuch display can be made to the patient, caregiver, physician, or otherhealth care professional.

In many embodiments, unique demographic and characteristic informationfrom a patient can be input into an assessment system (input peripheralof the adherent device system). The information input to the system canmodify measured values in accordance with an algorithm, select anappropriate algorithm parameter range or select a separate algorithmsub-routine that would be most appropriate for that specific patientwith a specific disease condition, or combinations thereof. Thisinformation is useful for incorporation into a disease tracking orprediction algorithm implemented in accordance with instructions of acomputer program. The input information can be used to set the initialconditions of an event prediction algorithm, and can influence thecharacteristics and/or thresholds of the algorithm.

In many embodiments, the input information can be displayed to thephysician relative to values that are appropriate for that patient. Thisdisplay allows the physician to have an assessment of a patient statusrelative to a range of values that are appropriate and specific for thatpatient. Deviation from such values can signify and quantify a change inpatient status such as either an improvement or worsening in diseasecondition in a manner that is specific to that patient. This patientspecific information is useful for long term monitoring of patientcondition, medication compliance and disease stability.

As used herein the term data encompasses information.

FIG. 1 shows a patient P and a monitoring system 10. Patient P comprisesa midline M, a first side S1, for example a right side, and a secondside S2, for example a left side. Monitoring system 10 comprises apatient measurement device to monitor the patient which may comprise animplantable device 100I or an adherent device 100, for example. Adherentdevice 100 can be adhered to a patient P at many locations, for examplethorax T of patient P. In many embodiments, the adherent device mayadhere to one side of the patient, from which side data can becollected. Work in relation with embodiments of the present inventionsuggests that location on a side of the patient can provide comfort forthe patient while the device is adhered to the patient. The monitoringsystem 10 and adherent device 100 may comprise components as describedin U.S. Pub. No. US-2009-0076345-A1, entitled “Adherent Device withMultiple Physiological Sensors”, and U.S. Pub. No. US-2009-0076344-A1,entitled “Multi-sensor Patient Monitor to Detect Impending CardiacDecompensation”, the full disclosures of which are incorporated hereinby reference and suitable for combination in accordance with someembodiments of the present invention as described herein.

Adherent device 100 can wirelessly communicate with remote center 106.The communication may occur directly (via a cellular or Wi-Fi network),or indirectly through intermediate device or gateway 102. The gateway102 may comprise components of the zLink™, a small portable devicesimilar to a cell phone that wirelessly transmits information receivedfrom PiiX™ to Corventis, commercially available from Corventis Inc. ofSan Jose, Calif. The gateway 102 may consist of multiple devices, whichcan communicate wired or wirelessly with remote center 106 in many ways,for example with a connection 104 which may comprise an Internetconnection and/or with a cellular connection. Remote center 106 maycomprise Corventis Web Services, a hosted application for data analysisand storage that also includes the Corventis website(www.corventis.com), which enables secure access to physiological trendsand clinical event information for interpretation and diagnosis. In manyembodiments, monitoring system 10 comprises a distributed processorsystem with at least one processor comprising a tangible medium ofdevice 100, at least one processor 102P of gateway 102, and at least oneprocessor 106P at remote center 106, each of which processors can be inelectronic communication with the other processors. At least oneprocessor 102P comprises a tangible medium 102T, and at least oneprocessor 106P comprises a tangible medium 106T. Remote processor 106Pmay comprise a backend server located at the remote center. Remotecenter 106 can be in communication with a health care providercommunication device 108A with a communication system 107A, such as theInternet, an intranet, phone lines, wireless and/or satellite phone.Health care provider communication device 108A, for example for a familymember, can be in communication with patient P with a communication, asindicated by arrow 109A. Remote center 106 can be in communication witha health care professional, for example with a physician 108Bcommunication device, with a communication system 107B, such as theInternet, an intranet, phone lines, wireless and/or satellite phone.Physician communication device 108B can be in communication with patientP with communication, for example with a two way communication system,as indicated by arrow 109B. The PDA may comprise a tangible mediumhaving instruction of a computer program embodied thereon to display thepatient data to the physician. Remote center 106 can be in communicationwith an emergency responder device 108C, for example a communicationdevice for a 911 operator and/or paramedic, with a communication system107C. In many embodiments, instructions are transmitted from remote site106 to a processor supported with the adherent patch on the patient, andthe processor supported with the patient can receive updatedinstructions for the patient treatment and/or monitoring, for examplewhile worn by the patient. Emergency responder device 108C can travelwith the responder to the patient as indicated by arrow 109C. Thus, inmany embodiments, monitoring system 10 comprises a closed loop system inwhich patient care can be monitored and implemented from the remotecenter in response to signals from the adherent device.

Each of the above described communication devices may comprise a displaycoupled to a processor having a tangible medium comprising a memory withinstructions of a computer program embodied thereon, for example apersonal digital assistant (PDA) such as a smart phone, for example aiPhone™, or Blackberry™.

In many embodiments, adherent device 100 may continuously monitorphysiological parameters, communicate wirelessly with a remote center,and provide alerts when necessary. The adherent patch may attach to thepatient's thorax and contains sensing electrodes, battery, memory,logic, and wireless communication capabilities. In some embodiments,remote center 106 receives the patient data and applies a patientevaluation algorithm, for example the prediction algorithm to predictpatient physiological or mental deterioration. In some embodiments, thealgorithm may comprise an algorithm to predict impending patientphysiological or mental deterioration, for example based on decreasedhydration and activity. When a flag is raised, the center maycommunicate with the patient, hospital, nurse, and/or physician to allowfor therapeutic intervention, for example to prevent furtherphysiological or mental deterioration.

Adherent device 100 may be affixed and/or adhered to the body in manyways. For example, with at least one of the following an adhesive tape,a constant-force spring, suspenders around shoulders, a screw-inmicroneedle electrode, a pre-shaped electronics module to shape fabricto a thorax, a pinch onto roll of skin, or transcutaneous anchoring.Patch and/or device replacement may occur with a keyed patch (e.g.two-part patch), an outline or anatomical mark, a low-adhesive guide(place guide|remove old patch|place new patch|remove guide), or a keyedattachment for chatter reduction. The patch and/or device may comprisean adhesiveless embodiment (e.g. chest strap), and/or a low-irritationadhesive for sensitive skin. The adherent patch and/or device cancomprise many shapes, for example at least one of a dogbone, anhourglass, an oblong, a circular or an oval shape.

In many embodiments, adherent device 100 may comprise a reusableelectronics module with replaceable disposable patches, and each of thereplaceable patches may include a battery. The adherent device 100 maycomprise components of the PiiX™, an unobtrusive, water-resistant,patient-worn device that adheres to the skin and automatically collectsand transmits physiological information, commercially available fromCorventis Inc. of San Jose, Calif. In some embodiments, the device mayhave a rechargeable module, and may use dual battery and/or electronicsmodules, wherein one module 101A can be recharged using a chargingstation 103 while the other module 101B is placed on the adherent patchwith connectors. In some embodiments, the gateway 102 may comprise thecharging module, data transfer, storage and/or transmission, such thatone of the electronics modules can be placed in the gateway 102 forcharging and/or data transfer while the other electronics module is wornby the patient.

System 10 can perform the following functions: initiation, programming,measuring, storing, analyzing, communicating, predicting, anddisplaying. The adherent device may contain a subset of the followingphysiological sensors: bioimpedance, respiration, respiration ratevariability, heart rate (ave, min, max), heart rhythm, heart ratevariability (HRV), heart rate turbulence (HRT), heart sounds (e.g. S3),respiratory sounds, blood pressure, activity, posture, wake/sleep,orthopnea, temperature/heat flux, and weight. The activity sensor maycomprise one or more of the following: ball switch, accelerometer,minute ventilation, HR, bioimpedance noise, skin temperature/heat flux,BP, muscle noise, posture.

FIG. 1A shows a first patient suitable for monitoring.

FIG. 1B shows a second patient suitable for monitoring.

FIG. 1C shows a third patient suitable for monitoring.

FIG. 1D shows a fourth patient suitable for monitoring.

Each of the patients shown in FIGS. 1A to 1D may have different physicalattributes, such that it can be helpful to determine the output based onthe data of the patient. For example, the patients shown in FIGS. 1A and1B comprise men and the patients shown in FIGS. 1C and 1D comprisewomen. Each of the patients may have a body mass index determined basedon the height and weight of the patient.

Work in relation to embodiments indicates patient characteristics caninfluence the measurements of the patient. For example, patientdemographics such as age, gender and race can be related to themeasurements of the patient.

Physiology of the patient can also influence the measurements. Forexample fat comprising adipose tissue, fat molecules, fat cells andcombinations thereof can influence impedance measurements. The fat maycomprise a layer of tissue disposed under the skin that can influencethe impedance measured through the skin of the patient. For example,electrical current passed through the fat tissue can increase themeasured impedance of the patient. Alternatively or in combination, thefat can be disposed in the internal tissues of the patient. For example,fat molecules can permeate internal tissues and may influence impedancemeasurements of implanted electrodes passing an electrical currentthrough the internal tissues or of electrodes disposed on the skinpassing current through the internal tissue. For example, fat mayincrease the measured impedance of a patient having normal hydrationsuch that the measured impedance is abnormally high, for example, whenthe patient has normal hydration and would appear dehydrated based on animpedance measurement alone.

FIGS. 2A and 2A1 show a side cross-sectional view and an exploded view,respectively, of embodiments of the adherent device. The adherent device100 may comprise an adherent patch 110 with an adhesive 116B, electrodes112A, 112B, 112C, 112D with gels 114A, 114B, 114C, 114D, gel cover 180,temperature sensor 177, cover 162, and a printed circuit board (PCB) 120with various circuitry for monitoring physiological sensors,communicating wirelessly with a remote center, and providing alerts whennecessary. The adherent device 100 comprises at least two electrodescomprising two or more of electrodes 112A, 112B, 112C and 112D. Adherentdevice 100 may comprise a maximum dimension, for example a maximumlength from about 4 to 10 inches, a maximum thickness along a profile ofthe device from about 0.2 inches to about 0.6 inches, and a maximumwidth from about 2 to about 4 inches.

The adherent patch 110 comprises a first side, or a lower side 110A,that is oriented toward the skin of the patient when placed on thepatient. The adherent patch 110 may also comprise a tape 110T which is amaterial, preferably breathable, with an adhesive 116A to adhere topatient P. Electrodes 112A, 112B, 112C and 112D are affixed to adherentpatch 110. In many embodiments, at least four electrodes are attached tothe patch. Gels 114A, 114B, 114C and 114D can each be positioned overelectrodes 112A, 112B, 112C and 112D, respectively, to provideelectrical conductivity between the electrodes and the skin of thepatient. Adherent patch 100 also comprises a second side, or upper side110B. In many embodiments, electrodes 112A, 112B, 112C and 112D extendfrom lower side 110A through adherent patch 110 to upper side 110B. Anadhesive 116B can be applied to upper side 110B to adhere structures,for example a breathable cover, to the patch such that the patch cansupport the electronics and other structures when the patch is adheredto the patient.

In many embodiments, adherent patch 110 may comprise a layer ofbreathable tape 110T, for example a tricot-knit polyester fabric, toallow moisture vapor and air to circulate to and from the skin of thepatient through the tape. In many embodiments, breathable tape 110Tcomprises a backing material, or backing 111, with an adhesive. In manyembodiments, the backing is conformable and/or flexible, such that thedevice and/or patch does not become detached with body movement. In manyembodiments, the adhesive patch may comprise from 1 to 2 pieces, forexample 1 piece. In many embodiments, adherent patch 110 comprisespharmacological agents, such as at least one of beta blockers, aceinhibiters, diuretics, steroid for inflammation, antibiotic, antifungalagent, and cortisone steroid. Patch 110 may comprise many geometricshapes, for example at least one of oblong, oval, butterfly, dogbone,dumbbell, round, square with rounded corners, rectangular with roundedcorners, or a polygon with rounded corners. In specific embodiments, athickness of adherent patch 110 is within a range from about 0.001″ toabout 0.020″, length of the patch is within a range from about 2″ toabout 10″, and width of the patch is within a range from about 1″ toabout 5″.

In many embodiments, the adherent device 100 comprises a temperaturesensor 177 disposed over a peripheral portion of gel cover 180 to allowthe temperature near the skin to be measured through the breathable tapeand the gel cover. Temperature sensor 177 can be affixed to gel cover180 such that the temperature sensor can move when the gel coverstretches and tape stretch with the skin of the patient. Temperaturesensor 177 may be coupled to temperature sensor circuitry 144 through aflex connection comprising at least one of wires, shielded wires,non-shielded wires, a flex circuit, or a flex PCB. The temperaturesensor can be affixed to the breathable tape, for example through acutout in the gel cover with the temperature sensor positioned away fromthe gel pads. A heat flux sensor can be positioned near the temperaturesensor for example to measure heat flux through to the gel cover.

The adherent device comprises electrodes 112A, 112B, 112C and 112Dconfigured to couple to tissue through apertures in the breathable tape110T. Electrodes 112A, 112B, 112C and 112D can be fabricated in manyways, for example printed on a flexible connector 112F, such as silverink on polyurethane. In some embodiments, the electrodes may comprise atleast one of carbon-filled ABS plastic, Ag/AgCl, silver, nickel, orelectrically conductive acrylic tape. The electrodes may comprise manygeometric shapes to contact the skin, for example at least one ofsquare, circular, oblong, star shaped, polygon shaped, or round. Inspecific embodiments, a dimension across a width of each electrodes iswithin a range from about 002″ to about 0.050″. In specific embodiments,the two inside electrodes may comprise force, or current electrodes,with a center to center spacing within a range from about 20 to about 50mm. In specific embodiments, the two outside electrodes may comprisemeasurement electrodes, for example voltage electrodes, and acenter-center spacing between adjacent voltage and current electrodes iswithin a range from about 15 mm to about 35 mm. Therefore, in manyembodiments, a spacing between inner electrodes may be greater than aspacing between an inner electrode and an outer electrode.

In many embodiments, gel 114A, or gel layer, comprises a hydrogel thatis positioned on electrode 112A and provides a conductive interfacebetween skin and electrode, so as to reduce impedance betweenelectrode/skin interface. The gel may comprise water, glycerol, andelectrolytes, pharmacological agents, such as beta blockers, aceinhibiters, diuretics, steroid for inflammation, antibiotic, andantifungal agents. Gels 114A, 114B, 114C and 114D can be positioned overelectrodes 112A, 112B, 112C and 112D, respectively, so as to coupleelectrodes to the skin of the patient. The flexible connector 112Fcomprising the electrodes can extend from under the gel cover to the PCBto connect to the PCB and/or components supported thereon. For example,flexible connector 112F may comprise flexible connector 122A to providestrain relief.

A gel cover 180, or gel cover layer, for example a polyurethanenon-woven tape, can be positioned over patch 110 comprising thebreathable tape to inhibit flow of gels 114A-114D through breathabletape 110T. Gel cover 180 may comprise at least one of a polyurethane,polyethylene, polyolefin, rayon, PVC, silicone, non-woven material,foam, or a film. Gel cover 180 may comprise an adhesive, for example anacrylate pressure sensitive adhesive, to adhere the gel cover toadherent patch 110. In many embodiments, the gel cover can regulatemoisture of the gel near the electrodes so as to keeps excessivemoisture, for example from a patient shower, from penetrating gels nearthe electrodes. A PCB layer, for example the flex PCB 120, or flex PCBlayer, can be positioned over gel cover 180 with electronic components130 connected and/or mounted to the flex PCB 120, for example mounted onflex PCB so as to comprise an electronics layer disposed on the flex PCBlayer. In many embodiments, the gel cover may avoid release of excessivemoisture form the gel, for example toward the electronics and/or PCBmodules. In many embodiments, a thickness of gel cover is within a rangefrom about 0.0005″ to about 0.020″. In many embodiments, gel cover 180can extend outward from about 0-20 mm from an edge of gels. Gel layer180 and breathable tape 110T comprise apertures 180A, 180B, 180C and180D through which electrodes 112A-112D are exposed to gels 114A-114D.

In many embodiments, device 100 includes a printed circuitry, forexample a PCB module that includes at least one PCB with electronicscomponent mounted thereon. The printed circuit may comprise polyesterfilm with silver traces printed thereon. Rigid PCB's 120A, 120B, 120Cand 120D with electronic components may be mounted on the flex PCB 120.In many embodiments, the PCB module comprises two rigid PCB modules withassociated components mounted therein, and the two rigid PCB modules areconnected by flex circuit, for example a flex PCB. In specificembodiments, the PCB module comprises a known rigid FR4 type PCB and aflex PCB comprising known polyimide type PCB. Batteries 150 may bepositioned over the flex PCB and electronic components. Batteries 150may comprise rechargeable batteries that can be removed and/orrecharged. A cover 162 may be placed over the batteries, electroniccomponents and flex PCB. In specific embodiments, the PCB modulecomprises a rigid PCB with flex interconnects to allow the device toflex with patient movement. The geometry of flex PCB module may comprisemany shapes, for example at least one of oblong, oval, butterfly,dogbone, dumbbell, round, square, rectangular with rounded corners, orpolygon with rounded corners. In specific embodiments the geometricshape of the flex PCB module comprises at least one of dogbone ordumbbell. The PCB module may comprise a PCB layer with flex PCB 120 thatcan be positioned over gel cover 180 and electronic components 130connected and/or mounted to flex PCB 120. In many embodiments, theadherent device may comprise a segmented inner component, for examplethe PCB, for limited flexibility.

In many embodiments, an electronics housing 160 encapsulates theelectronics layer. Electronics housing 160 may comprise an encapsulant,such as a dip coating, which may comprise a waterproof material, forexample silicone, epoxy, other adhesives and/or sealants. In manyembodiments, the PCB encapsulant protects the PCB and/or electroniccomponents from moisture and/or mechanical forces. The encapsulant maycomprise silicone, epoxy, other adhesives and/or sealants. In someembodiments, the electronics housing may comprising metal and/or plastichousing and potted with aforementioned sealants and/or adhesives.

In many embodiments, cover 162 can encase the flex PCB, electronics,and/or adherent patch 110 so as to protect at least the electronicscomponents and the PCB. In some embodiments, cover 162 can be adhered toadherent patch 110 with an adhesive 164 or adhesive 116B on an undersideof cover 162. In many embodiments, cover 162 attaches to adherent patch110 with adhesive 116B, and cover 162 is adhered to the PCB module withan adhesive 161 on the upper surface of the electronics housing. Cover162 can comprise many known biocompatible cover materials, for examplesilicone, an outer polymer cover to provide smooth contour withoutlimiting flexibility, a breathable fabric, or a breathable waterresistant cover. In some embodiments, the breathable fabric may comprisepolyester, nylon, polyamide, and/or elastane (Spandex™). Work inrelation to embodiments of the present invention suggests that thesecoatings can be important to keep excessive moisture from the gels nearthe electrodes and to remove moisture from body so as to provide patientcomfort.

In many embodiments, cover 162 can be attached to adherent patch 110with adhesive 116B such that cover 162 stretches and/or retracts whenadherent patch 110 stretches and/or retracts with the skin of thepatient. For example, cover 162 and adherent patch 110 can stretch intwo dimensions along the length and width of the adherent patch with theskin of the patient, and stretching along the length can increasespacing between electrodes. Stretching of the cover and adherent patch110 can extend the time the patch is adhered to the skin as the patchcan move with the skin. Electronics housing 160 can be smooth and allowbreathable cover 162 to slide over electronics housing 160, such thatmotion and/or stretching of cover 162 is slidably coupled with housing160. The PCB can be slidably coupled with adherent patch 110 thatcomprises breathable tape 110T, such that the breathable tape canstretch with the skin of the patient when the breathable tape is adheredto the skin of the patient, for example along two dimensions comprisingthe length and the width.

The breathable cover 162 and adherent patch 110 comprise breathable tapethat can be configured to couple continuously for at least one week theat least one electrode to the skin so as to measure breathing of thepatient. The breathable tape may comprise the stretchable breathablematerial with the adhesive and the breathable cover may comprises astretchable breathable material connected to the breathable tape, asdescribed above, such that both the adherent patch and cover can stretchwith the skin of the patient. Arrows 182 show stretching of adherentpatch 110, and the stretching of adherent patch can be at least twodimensional along the surface of the skin of the patient. As notedabove, connectors 122A-122D between PCB 130 and electrodes 112A-112D maycomprise insulated wires that provide strain relief between the PCB andthe electrodes, such that the electrodes can move with the adherentpatch as the adherent patch comprising breathable tape stretches. Arrows184 show stretching of cover 162, and the stretching of the cover can beat least two dimensional along the surface of the skin of the patient.

The PCB 120 may be adhered to the adherent patch 110 comprisingbreathable tape 110T at a central portion, for example a single centrallocation, such that adherent patch 110 can stretched around this centralregion. The central portion can be sized such that the adherence of thePCB to the breathable tape does not have a substantial effect of themodulus of the composite modulus for the fabric cover, breathable tapeand gel cover, as described above. For example, the central portionadhered to the patch may be less than about 100 mm², for example withdimensions that comprise no more than about 10% of the area of patch110, such that patch 110 can stretch with the skin of the patient.Electronics components 130, PCB 120, and electronics housing 160 arecoupled together and disposed between the stretchable breathablematerial of adherent patch 110 and the stretchable breathable materialof cover 160 so as to allow the adherent patch 110 and cover 160 tostretch together while electronics components 130, PCB 120, andelectronics housing 160 do not stretch substantially, if at all. Thisdecoupling of electronics housing 160, PCB 120 and electronic components130 can allow the adherent patch 110 comprising breathable tape to movewith the skin of the patient, such that the adherent patch can remainadhered to the skin for an extended time of at least one week.

An air gap 169 may extend from adherent patch 110 to the electronicsmodule and/or PCB, so as to provide patient comfort. Air gap 169 allowsadherent patch 110 and breathable tape 110T to remain supple and move,for example bend, with the skin of the patient with minimal flexingand/or bending of PCB 120 and electronic components 130, as indicated byarrows 186. PCB 120 and electronics components 130 that are separatedfrom the breathable tape 110T with air gap 169 can allow the skin torelease moisture as water vapor through the breathable tape, gel cover,and breathable cover. This release of moisture from the skin through theair gap can minimize, and even avoid, excess moisture, for example whenthe patient sweats and/or showers. Gap 169 extends from adherent patch110 to the electronics module and/or PCB a distance within a range fromabout 0.25 mm to about 4 mm.

In many embodiments, the adherent device comprises a patch component andat least one electronics module. The patch component may compriseadherent patch 110 comprising the breathable tape with adhesive coating116A, at least one electrode, for example electrode 112A and gel 114A.The at least one electronics module can be separable from the patchcomponent. In many embodiments, the at least one electronics modulecomprises the flex PCB 120, electronic components 130, electronicshousing 160 and cover 162, such that the flex PCB, electroniccomponents, electronics housing and cover are reusable and/or removablefor recharging and data transfer, for example as described above. Inspecific embodiments, the electronic module can be adhered to the patchcomponent with a releasable connection, for example with Velcro™, aknown hook and loop connection, and/or snap directly to the electrodes.Monitoring with multiple adherent patches for an extended period isdescribed in U.S. Pub. No. 2009-0076345-A1, published on Mar. 19, 2009,the full disclosure of which has been previously incorporated herein byreference, and which adherent patches and methods are suitable forcombination in accordance with embodiments described herein.

The adherent device 100, shown in FIG. 2A, may comprise an X-axis,Y-axis and Z-axis for use in determining the orientation of the adherentdevice 100 and/or the patient P. Electric components 130 may comprise a3D accelerometer. As the accelerometer of adherent device 100 can besensitive to gravity, inclination of the patch relative to an axis ofthe patient can be measured, for example when the patient stands.Vectors from a 3D accelerometer can be used to determine the orientationof a measurement axis of the patch adhered on the patient and can beused to determine the angle of the patient, for example whether thepatient is laying horizontally or standing upright, when measuredrelative to the X-axis, Y-axis and/or X-axis of adherent device 100.

FIG. 2B shows a PCB and electronic components over adherent patch 110.In some embodiments, PCB 120, for example a flex PCB, may be connectedto electrodes 112A, 112B, 112C and 112D of FIG. 2A with connectors 122A,122B, 122C and 122D, respectively, and may include traces 123A, 123B,123C and 123D that extend to connectors 122A, 122B, 122C and 122D. Insome embodiments, connectors 122A-122D may comprise insulated wiresand/or a film with conductive ink that provide strain relief between thePCB and the electrodes. Examples of structures to provide strain reliefare also described in U.S. Pub. No. 2009-0076345-A1, entitled “AdherentDevice with Multiple Physiological Sensors”, filed on Sep. 12, 2008 asnoted above.

Electronic components 130 comprise components to take physiologicmeasurements, transmit data to remote center 106 and receive commandsfrom remote center 106. In many embodiments, electronics components 130may comprise known low power circuitry, for example complementary metaloxide semiconductor (CMOS) circuitry components. Electronics components130 comprise a temperature sensor, an activity sensor and activitycircuitry 134, impedance circuitry 136 and electrocardiogram circuitry,for example ECG circuitry 138. In some embodiments, electronicscircuitry 130 may comprise a microphone and microphone circuitry 142 todetect an audio signal, such as heart or respiratory sound, from withinthe patient.

Electronics circuitry 130 may comprise a temperature sensor, for examplea thermistor in contact with the skin of the patient, and temperaturesensor circuitry 144 to measure a temperature of the patient, forexample a temperature of the skin of the patient. A temperature sensormay be used to determine the sleep and wake state of the patient, whichmay decrease during sleep and increase during waking hours. Work inrelation to embodiments of the present invention suggests that skintemperature may affect impedance and/or hydration measurements, and thatskin temperature measurements may be used to correct impedance and/orhydration measurements. In some embodiments, increase in skintemperature or heat flux can be associated with increased vaso-dilationnear the skin surface, such that measured impedance measurementdecreased, even through the hydration of the patient in deeper tissuesunder the skin remains substantially unchanged. Thus, use of thetemperature sensor can allow for correction of the hydration signals tomore accurately assess the hydration, for example extra cellularhydration, of deeper tissues of the patient, for example deeper tissuesin the thorax.

Activity sensor and activity circuitry 134 can comprise many knownactivity sensors and circuitry. In many embodiments, the accelerometercomprises at least one of a piezoelectric accelerometer, capacitiveaccelerometer or electromechanical accelerometer. The accelerometer maycomprises a 3-axis accelerometer to measure at least one of aninclination, a position, an orientation or acceleration of the patientin three dimensions. Work in relation to embodiments of the presentinvention suggests that three dimensional orientation of the patient andassociated positions, for example sitting, standing, lying down, can bevery useful when combined with data from other sensors, for examplehydration data.

Impedance circuitry 136 can generate both hydration data and respirationdata. In many embodiments, impedance circuitry 136 is electricallyconnected to electrodes 112A, 112B, 112C and 112D of FIG. 2A in a fourpole configuration, such that electrodes 112A and 112D comprise outerelectrodes that are driven with a current and comprise force electrodesthat force the current through the tissue. The current delivered betweenelectrodes 112A and 112D generates a measurable voltage betweenelectrodes 112B and 112C, such that electrodes 112B and 112C compriseinner, sense, electrodes that sense and/or measure the voltage inresponse to the current from the force electrodes. In some embodiments,electrodes 112B and 112C may comprise force electrodes and electrodes112A and 112D may comprise sense electrodes. The voltage measured by thesense electrodes can be used to measure the impedance of the patient anddetermine the respiration rate and/or hydration of the patient. Theelectrocardiogram circuitry may be coupled to the sense electrodes tomeasure the electrocardiogram signal, for example as described in U.S.Pub. No. 2009-0076345-A1, entitled “Adherent Device with MultiplePhysiological Sensors”, published on Mar. 29, 2009, previouslyincorporated by reference and suitable for combination in accordancewith embodiments described herein. In many embodiments, impedancecircuitry 136 can be configured to determine respiration of the patient.In specific embodiments, the impedance circuitry can measure thehydration at 25 Hz intervals, for example at 25 Hz intervals usingimpedance measurements with a frequency from about 0.5 kHz to about 20kHz.

ECG circuitry 138 can generate electrocardiogram signals and data fromtwo or more of electrodes 112A, 112B, 112C and 112D in many ways. Insome embodiments, ECG circuitry 138 is connected to inner electrodes112B and 122C, which may comprise sense electrodes of the impedancecircuitry as described above. In many embodiments, the ECG circuitry maymeasure the ECG signal from electrodes 112A and 112D when current is notpassed through electrodes 112A and 112D.

Electronics circuitry 130 may comprise a processor 146 that can beconfigured to control a collection and transmission of data from theimpedance circuitry electrocardiogram circuitry and the accelerometer.Processor 146 comprises a tangible medium, for example read only memory(ROM), electrically erasable programmable read only memory (EEPROM)and/or random access memory (RAM). Electronic circuitry 130 may comprisereal time clock and frequency generator circuitry 148. In someembodiments, processor 146 may comprise the frequency generator and realtime clock. In many embodiments, device 100 comprises a distributedprocessor system, for example with multiple processors on device 100.

In many embodiments, electronics components 130 comprise wirelesscommunications circuitry 132 to communicate with remote center 106. PCB120 may comprise an antenna to facilitate wireless communication. Theantenna may be integral with PCB 120 or may be separately coupledthereto. The wireless communication circuitry can be coupled to theimpedance circuitry, the electrocardiogram circuitry and theaccelerometer to transmit to a remote center with a communicationprotocol at least one of the hydration signal, the electrocardiogramsignal or the inclination signal. In specific embodiments, wirelesscommunication circuitry 132 is configured to transmit the hydrationsignal, the electrocardiogram signal and the inclination signal to theremote center either directly or through gateway 102. The communicationprotocol comprises at least one of Bluetooth, ZigBee, WiFi, WiMAX, IR,amplitude modulation or frequency modulation. In many embodiments, thecommunications protocol comprises a two way protocol such that theremote center is capable of issuing commands to control data collection.

In many embodiments, the electrodes are connected to the PCB with a flexconnection, for example trace 123A, 123B, 123C and 123D of flex PCB 120,so as to provide strain relief between the electrodes 112A, 112B, 112Cand 112D and the PCB. In such embodiments, motion of the electrodesrelative to the electronics modules, for example rigid PCB's 120A, 120B,120C and 120D with the electronic components mounted thereon, does notcompromise integrity of the electrode/hydrogel/skin contact. In manyembodiments, the flex connection comprises at least one of wires,shielded wires, non-shielded wires, a flex circuit, or a flex PCB. Inspecific embodiments, the flex connection may comprise insulated,non-shielded wires with loops to allow independent motion of the PCBmodule relative to the electrodes.

FIG. 2B1 shows an equivalent circuit 152 that can be used to determineoptimal frequencies for measuring patient hydration. Work in relation toembodiments of the present invention indicates that the frequency of thecurrent and/or voltage at the force electrodes can be selected so as toprovide impedance signals related to the extracellular and/orintracellular hydration of the patient tissue. Equivalent circuit 152comprises an intracellular resistance 156, or R(ICW) in series with acapacitor 154, and an extracellular resistance 158, or R(ECW).Extracellular resistance 158 is in parallel with intracellularresistance 156 and capacitor 154 related to capacitance of cellmembranes. In many embodiments, impedances can be measured and provideuseful information over a wide range of frequencies, for example fromabout 0.5 kHz to about 200 KHz. Work in relation to embodiments of thepresent invention suggests that extracellular resistance 158 can besignificantly related extracellular fluid and to patient physiologicalor mental physiological or mental deterioration, and that extracellularresistance 158 and extracellular fluid can be effectively measured withfrequencies in a range from about 0.5 kHz to about 50 kHz, for examplefrom about 0.5 kHz to 20 kHz, for example from about 1 kHz to about 10kHz. In some embodiments, a single frequency can be used to determinethe extracellular resistance and/or fluid. As sample frequenciesincrease from about 10 kHz to about 20 kHz, capacitance related to cellmembranes decrease the impedance, such that the intracellular fluidcontributes to the impedance and/or hydration measurements. Thus, manyembodiments of the present invention measure hydration with frequenciesfrom about 0.5 kHz to about 50 kHz to determine patient hydration.

As noted herein, fat can influence the impedance measurement and anincrease in fat can increase the measured impedance and a decrease infat can decrease the measured impedance, and the presence of fat can berelated to the impedance measured with the equivalent circuit shown inFIG. 2B1.

FIG. 2C shows a schematic illustration of an implantable device 1001suitable for incorporation in accordance with embodiments of the presentinvention. Implantable device 1001 comprises at least two implantableelectrodes 1121, and a processor 146. The implantable device 1001 maycomprise a component of system 10, and processor 146 may comprise atleast one processor of the processor system. The implantable device 1001may comprise many of the components of the adherent device 100, asdescribed herein.

For example, the implantable device 1001 may comprise components of animplantable medical device as described in U.S. Pat. Pub. No.20080024293, entitled “ADAPTATIONS TO OPTIVOL ALERT ALGORITHM”, in thename of Stylos, published on Jan. 31, 2008. The implantable medicaldevice may include a hermetically sealed enclosure and three leads: aventricular lead, an atrial/SVC lead, and a coronary sinus/coronary veinlead. The enclosure may contain the electronic circuitry used forgenerating cardiac pacing pulses for delivering cardioversion anddefibrillation shocks and for monitoring the patient's heart rhythm.Examples of such circuitry are known in the art. The ventricular leadmay carry three electrodes adjacent its distal end: a ring electrode, anextendable helix electrode mounted retractably within an insulativeelectrode head, and an elongated coil electrode. The atrial/SVC lead maycarry the same three electrodes adjacent its distal end: a ringelectrode, an extendible helix electrode mounted retractably within aninsulative electrode head, and an elongated coil electrode. The coronarysinus/coronary vein lead may carry an electrode (illustrated in brokenoutline) that can be located within the coronary sinus and great vein ofthe heart. The coronary sinus/coronary vein lead may also carry a ringelectrode and a tip electrode adjacent its distal end.

FIG. 2D shows a viewable display showing an hydration indicator disposedbetween a first upper adjusted marker and a second lower adjustedmarker. The first upper marker may correspond to dehydration of thepatient can be shown in red a may correspond to a first threshold shownon a first region of the display. The second upper marker may correspondto excessive hydration of the patient can be shown in red a maycorrespond a first threshold shown on a second portion of the screen.The hydration indicator may comprise a slider disposed between the firstmarker and the second marker when the patient has normal hydration. Thehydration indicator may comprise an adjusted impedance corresponding toa personalized fluid level of the patient. For example, the patient'sheight and weight can be entered into the at least one processor of theprocessor system and used to determine a personalized bioimpedancecorresponding to a personalized fluid amount or fluid level. Themeasured bioimpedance can be compared to a heart failure patientpopulation with comparable physical characteristics. The graphicaldisplay can show the patient specific fluid status relative to acomparable cohort of patients.

The information can be used in many ways, for example to assess patientstatus as a point in time measurement, for example upon admission to ahospital or upon discharge from a hospital. The hydration indicatorcomprising the adjusted bioimpedance can be used to guide patient caresuch as diuresis and ultrafiltration and to assess response totreatment. The hydration indicator comprising the adjusted bioimpedancecan be used to assess remotely the stability of HF disease followingdischarge and to assess remotely patient compliance and effectivenesswith HF medication.

FIG. 2E shows a plot corresponding to statistical parameters of apatient population that can be combined with patient data to generate adisplay in accordance with embodiments shown in FIG. 2D, for example.The statistical data may correspond to statistics of a patientpopulation such as a regression coefficients that may comprise a slope,offset and fit coefficients that can be used to determine a regressionline, confidence intervals and prediction intervals, for example. Thedisplay can show the statistical data of the population and theindividual patient, so as to help the physician interpret the conditionof the patient. For example, the display can show a confidence interval,for example 95% CI, a prediction interval, for example 95% PI and aregression line. The prediction intervals may correspond to the firstupper marker and second lower marker shown above, for example, adjustedbased on the independent patient parameter such as body mass index.

The data can be stratified based on demographics such as a one or moreof race, gender and age, and correspondence among measured patientdetermined for presentation to the physician. The correspondence ofimpedance with body mass index can be used to establish the upper andlower markers and “optimal” hydration. For example, the patient bodymass index (BMI) and patient population data can be used to determinethe prediction interval markers corresponding to the upper and lowermarkers based on the body mass index, and the regression line can beused to determine the “optimal” hydration based on the body mass index.Similar adjustments can be made to many of the individual patientmeasurements as described herein based on the patient population data.

FIG. 2F shows adjusted impedance over time show on a physician devicedisplay in accordance with embodiments of FIG. 2D and FIG. 2E. Theadjusted impedance over time may comprise a plurality of adjustedimpedance measurements, for example daily measurements shown over aboutfour weeks. The adjusted impedance over time may be shown in relation tothe upper adjusted marker (too dry) and the lower adjusted marker (toowet). The time course of the patient treatment can be used by thephysician to evaluate treatment.

FIG. 3 shows a method 300 of monitoring a patient according toembodiments of the present invention

At a step 305, the impedance of a patient population is measured, forexample with a patient measurement device for each patient of thepopulation as described herein.

At a step 310, demographics of the individuals of the patient populationare measured.

At a step 315, additional data of patients of the population aremeasured (e.g. height, weight, blood chemistry, ejection fraction) suchthat correspondence of among the measurement data, demographic data andcorrespondence data can be determined.

At a step 320, statistical parameters of the patient population aredetermined.

At a step 325, statistical parameters of the patient population arestratified based on demographics.

At a step 330, the demographics of the individual patient aredetermined.

At a sub-step, 330A the race of the patient is determined.

At a sub-step 330B, the gender of the patient is determined.

At a sub-step 330C, the age of the patient is determined.

At a step 335 the additional data of the patient is determined (e.g.height, weight, blood chemistry, ejection fraction)

At a sub-step 335A, the height of the patient is determined.

At a sub-step 335B, the weight of the patient is determined.

At a sub-step 335C, the body mass index of the patient is determined.

At a sub-step 335D, the ejection fraction of the patient is determined.

At a step 340, electrodes are positioned in contact with the patient.

At a step 345, the impedance of the patient is measured, for examplewith electrodes as described above.

At a step 350, an adjusted impedance of the patient is determined, forexample a hydration indicator as described above.

At a step 355A, an upper adjusted impedance marker is determined,

At a step 355B, a lower adjusted impedance marker is determined.

At a step 360, display parameters are determined for the adjustedimpedance and markers.

At a step 365, spatial relationships are determined for the adjustedimpedance in relation to the adjusted impedance markers

At a step 370, a color is determined of the adjusted impedance based onthe spatial relationship to the markers, for example.

At a step 375, the adjusted impedance the upper adjusted marker and thelower adjusted marker are shown on the display to the physician.

At a step 380, a plurality of adjusted impedance measurements aredetermined.

At a step 385, adjusted impedance measurements over time are displayed.

At a step 390, adjusted impedance is combined with patient data topredict an event of the patient, for example a heart failure event ofthe patient.

At a step 395, the patient is treated with therapy, which may comprisetreatment with drugs, electrical stimulation therapy, or combinationsthereof.

At a step 397, the above steps can be repeated.

The processor system, as described above, may comprise instructions of acomputer program embedded thereon so as to perform many of the steps ofthe method 300. For example, many of the steps of the method 300 can beperformed with the processor system comprising the processor of thepatient measurement device, the processor of the gateway and theprocessor of the remote server. The method 300 can be performed with oneor more of the processor of the patient measurement device, theprocessor of the gateway and the processor of the remote server. Furtherthe steps of the method 300 can be distributed among the processor ofthe processor system such that each processor performs at least one ofthe steps or sub-steps of method 300.

It should be appreciated that the specific steps illustrated in FIG. 3provide a particular method of monitoring a patient in accordance withan embodiment of the present invention. Other sequences of steps mayalso be performed in accordance with alternative embodiments. Forexample, alternative embodiments of the present invention may performthe steps outlined above in a different order. Moreover, the individualsteps illustrated in FIG. 3 may include multiple sub-steps that may beperformed in various sequences as appropriate to the individual step.Alternatively, the multiple sub-steps may be performed as an individualstep. Furthermore, additional steps may be added or removed depending onthe particular applications. One of ordinary skill in the art wouldrecognize many variations, modifications, and alternatives.

Embodiments as described herein can be incorporated with manycommercially available patient monitoring and treatment systems such asthe OptiVol™ alert algorithm and computer programs embodyinginstructions thereof commercially available from Medtronic, theCareLink™ server commercially available from Medtronic, the Latitude™patient management system commercially available from Boston Scientific,the Merlin™ system commercially available from St. Jude and the MCOT™commercially available from CardioNet.

Cardiac Event Prediction

According to another aspect, embodiments may find particular applicationin the prediction of impending cardiac events, for example impendingacute decompensated heart failure (ADHF) events. In particular, theaccuracy of cardiac event prediction may be significantly enhanced ifthe prediction is based at least in part on personalized data gatheredfrom a patient, rather than being based merely on norms applicable tolarge patient populations. In some embodiments, the prediction is basedat least in part on data measured over time from the patient, forexample impedance data. The prediction may also be based at least inpart on descriptive data that indicates at least one descriptivecharacteristic of the patient, for example any one or any combination ofthe patient's height, weight, body mass index, or other characteristics.

An adherent device, for example an adherent device such as thosedescribed above and shown in FIGS. 2A-2B1, may be adhered to the skin ofa patient, and measures a bioimpedance between two or more electrodes ofthe adherent device. Bioimpedance may also be referred to herein assimply “impedance.” The adherent device provides ongoing impedancemeasurements that can be used to characterize the patient's hydrationlevel, breathing, and other parameters, and to monitor changes in thepatient's parameters. One or more of these parameters may be utilized topredict an impending cardiac event. Multi-parameter prediction may haveadvantages in event prediction accuracy.

As is discussed above, each adherent device may have a finite usefullife, and multiple adherent devices may be used serially to monitor apatient over an extended period. For example, each adherent device mayremain in place on the patient for about 1 week, after which anotheradherent device replaces the former one. The data from the multipleadherent devices may be combined into a single, ongoing data set. As isdescribed above, data from the adherent devices may be transmitted to aremote computer where analysis is performed.

The adherent device or devices may make ongoing measurements of a numberof different characteristics of the patient, including the patient'sbioimpedance, electrocardiogram information, temperature, activitylevel, posture, hydration, or other characteristics, such as an audiosignal characterizing a heart or respiratory sound of the patient.

Some patient data may indicate one or more descriptive characteristicsof the patient. This kind of data may also be referred to as “patientdescriptive data”. For example, patient descriptive data may correspondto the amount of fat in the patient's body, and could include one ormore of a percent body fat of the patient, a body mass index, a heightof the patient, a weight of the patient, or other informationcorresponding to the fat of the patient. Patient descriptive data mayinclude demographic information about the patient, for example thepatient's sex, race, or age.

In some embodiments, patient descriptive data may be input to thesystem, for example using a keyboard, key pad, bar code reader, scanner,wired or wireless electronic signal, automated measurement device, orother input mechanism. Patient descriptive data could also be obtainedfrom the patient's medical records, for example by automatic access ofmedical records stored electronically. Patient descriptive data may beinput into the system once at the beginning of a monitoring period, oronly occasionally during a monitoring period, as compared with thecharacteristics measured by the adherent devices on an ongoing basisthroughout the monitoring period.

FIG. 5 illustrates reading impedance data (BioZ) from the adherentdevice in accordance with embodiments, to determine one reading of animpedance parameter and one reading of a breath parameter. In someembodiments, impedance data from the adherent device are gatheredperiodically, for example every 5, 10, 15, 20, 30, or 60 minutes, todetermine the impedance parameter and the breath parameter. Otherintervals between readings may be used. The impedance may be indicativeof patient hydration, and the impedance data may also be processed toderive the breath parameter that is indicative of some aspect orcombination of aspects of the patient's breathing, for example thepatient's breathing rate or the volume of air breathed by the patient ina tidal breath.

In the example of FIG. 5, the impedance is sampled 120 times over thespan of 30 seconds, to gather 120 impedance data points. The raw datapoints from one 30-second sample period are shown in curve 501. Othersampling periods and frequencies could be used. The peaks and valleys incurve 501 correspond to the patient's breathing, and in this particularinstance, the patient took about seven breaths while the impedance wasbeing sampled.

To determine one impedance reading from the 120 data points, outliersmay be eliminated from the data points, and the median of the remainingpoints determined. In the example of FIG. 5, the impedance parameter IP502 derived from this 30-second sampling period is the median of theretained data points. In other embodiments, the impedance data pointscould be averaged to determine the impedance parameter, or some otherprocess could be used to determine the impedance parameter reading fromthe data points.

To determine the breath parameter for a particular sampling interval,curve 501 may be first filtered, to smooth the curve in a manner similarto filtered curve 503. In some embodiments, the filtering may beaccomplished by convolving a 5^(th) order high pass Butterworth filterwith a 0.05 Hz cutoff and a 9^(th) order low pass Butterworth filterwith a 0.4 Hz cutoff and applying the resulting filter function to thedata points, although other kinds of filters could be used. The DCcomponent of curve 501 may also be established at axis 504. The localmaxima of the absolute value of the filtered points are found, and thepeaks having magnitudes in the upper 50% of the peaks are retained—thatis, those having the largest magnitudes as measured from axis 504. Inthe example of FIG. 5, seven peaks 505 are retained. An envelope isdefined around each of the peaks, including all of the samples betweenthe respective peak and the neighboring minimum on each side of thepeak. A variance of these samples is then computed. In FIG. 5, thesamples used for computing the shown with heavy dots. One particulari^(th) sample 506 is labeled, and its magnitude e_(i) is depicted, asmeasured from axis 504. The variance is simply the sum of the squares ofthe magnitudes of the dotted samples. That is, the breath parameter BPderived from this 30-second sampling period is Σe_(i) ² over all of thedotted data points. The breath parameter BP may be related to thepatient's breath volume or breathing effort. The breath parameter valuesmay be further filtered before use. For example, in some embodiments,the breath parameter values are low-pass filtered before use, using anaveraging finite impulse response filter of length 20.

Thus, in this embodiment, at the end of each 30-second samplinginterval, a single reading of impedance parameter IP and a singlereading of breath parameter BP are obtained.

While specific example techniques for computing an impedance parameterand a breath parameter are described above, may other techniques arepossible within the scope of the claims. For example, the breathparameter could be computed as an area under curve 501 or curve 503, orthe breath parameter could be based on the peak values of curve 501 orcurve 503. Techniques for computing an impedance parameter and a breathparameter without generating a curve similar to curve 501 could beutilized. In some embodiments, a breath parameter relating to thepatient's breathing rate, breathing effort, or some other aspect of thepatient's breathing could be determined.

FIGS. 6 and 7 illustrate example methods of utilizing the impedanceparameter readings to derive a baseline impedance, an impedance index,and an impedance flag that may be used in predicting an impendingcardiac event.

In some embodiments, a prediction of an impending cardiac event is basedat least in part on relationship of the ongoing measurements of theimpedance parameter IP to a patient-specific baseline impedance. Becausebioimpedance and patient hydration are related, this parameter may bethought of as a measurement of the amount of fluid in the patient'stissues, and the prediction may be thought of as being based in part onthe relationship of the patient's ongoing fluid measurements to apatient-specific baseline fluid measurement.

Turning to FIG. 6, the upper curve 601 schematically represents theimpedance parameter readings taken a patient, as described above. Curve601 is simplified for ease of illustration, and spans about 90 days ofmonitoring. An actual curve including readings taken over several monthsmay include thousands of readings of impedance.

The impedance parameter readings taken during an initial period at thebeginning of monitoring may be used to determine one or morepatient-specific baseline values for the patient. For example, in someembodiments, a baseline impedance specific to the patient is computed asthe average of the impedance parameter readings taken during the initialperiod of monitoring. The length of the initial period may be selectedas any appropriate time, for example 24 hours, 48 hours, 72 hours, oranother suitable time period. The variability of the readings may alsobe characterized, for example the standard deviation of the impedanceparameter readings taken during the initial period may be computed. Someadditional filtering may be performed before computing the average andstandard deviation.

In some embodiments, patient activity data may be available, indicatingwhether the patient is resting or active, for example exercising. Theactivity data may be derived from signals provided by an accelerometeror other activity sensor on the adherent device that also performs theimpedance measurements. Preferably, the readings utilized forestablishing the baseline impedance (and the baseline breath parameterdescribed below) are taken while the patient is in a state of relativelylow activity, and preferably at rest. In other embodiments, thepatient's posture may be detected, and utilized in the data gathering.For example, readings utilized for establishing the baseline parametersmay preferably be taken while the patient is lying down.

An index may then be computed that is indicative of a change in theimpedance parameter relative to the baseline impedance over time. Forexample, the index may generally track the number of standard deviations(of the initial monitoring period data) by which the current impedanceparameter reading departs from the baseline impedance value. The indexis then compared with a threshold, and when the index passes thethreshold, an impedance flag is generated indicating that the impedancehas departed from the baseline value by more than the threshold amount.The flag may be an actual electronic signal, for example a voltage levelin a digital electronic circuit, but most often will be a staterecognized and recorded by a processor executing program instructions.

The lower curve 602 of FIG. 6 depicts an example impedance index and theresults of these computations. As can be seen, during the initial period603 while the baseline impedance is being established, the deviationfrom the baseline is taken to be zero. After the initial period 603, theimpedance index is tracked and compared with a threshold value. Thethreshold value may be set at any suitably predictive value, for example−0.6, −0.9, −1.2, −1.5, or another number of standard deviations of thebaseline data. The actual threshold value will depend on the particularmethod used for characterizing the impedance parameter readings duringthe initial period. For example if a variance of the impedance parameterreadings were to be used, then the magnitude of the threshold value maybe significantly different than in an embodiment where a standarddeviation is used. Any time the index passes or exceeds (goes below) thethreshold, the predictive impedance flag 604 indicates that thethreshold has been exceeded. This condition can be seen in FIG. 6 duringintervals 605. This impedance flag may be utilized alone or incombination with other flags or signals to predict an impending cardiacevent. Whenever the impedance index is closer than the threshold valueto the baseline impedance, the predictive impedance flag is not raised,for example in intervals 606 shown in FIG. 6.

Further filtering may be performed in the computation of the impedanceindex. In the exemplary embodiment of FIG. 6, the impedance index iscomputed periodically from the impedance parameter readings taken duringa preceding time window. Any suitable sampling period and window lengthmay be utilized. For example, the impedance index may be computed every¼ hour, ½ hour, 1 hour, 1.5 hour, 2 hours, or on another suitableschedule, and may be based on impedance parameter readings taken duringthe previous 12 hours, 24 hours, 36 hours, 48 hours, 60 hours, 72 hours,or another suitable window length. In some embodiments, the variabilityof the readings obtained during the current window is also considered inthe computation of the impedance index. Each window may overlapconsiderably with the previous one. For example, if the impedance indexis computer every ½ hour, and each window is 24 hours long, the eachwindow would overlap by 23.5 hours with the previous window.

FIG. 7 illustrates a flowchart of one exemplary embodiment for computingthe baseline impedance and the impedance index 602. In step 701, theimpedance parameter is monitored, for example as described above, forthe initial period. In step 702, the impedance parameter readings arefiltered. In some embodiments, a median filter of having an orderbetween 3 and 15, for example an order of 3, 5, 7, 9, 11, 13, or 15, maybe applied to the impedance parameter readings, although other kinds orlengths of filters may be used, or the filtering step may be omitted. Instep 703, a mean μ*_(B) and standard deviation σ*_(B) are computed fromthe filtered initial period impedance parameter readings. The meanμ*_(B) and standard deviation σ*_(B) are thus patient-specific baselinevalues derived from the patient's impedance data. A different patientmay have different values. The mean μ*_(B) is an example of a baselineimpedance specific to the patient.

In step 704, the impedance parameter is monitored for an additional timeperiod. In step 705, the data from the current window is examined to seeif gaps exist. For example, if it is determined that any two adjacentreadings were taken more than four hours apart, it may be determinedthat the data for the current window has gaps, and the data may not beused. In step 706, the readings in the current window are filtered, forexample using a 9^(th) order median filter or another kind of filter. Asbefore, the filtering step could be omitted. It will be also understoodthat some of the operations depicted in FIG. 7 may be reordered. Forexample, the filtering of the readings from the current window could beperformed before examining the data to see if gaps exist.

In step 707, the mean μ_(B,W) and standard deviation σ_(B,W) of filteredimpedance parameter data from the current window are computed. In step708, the impedance index T_(W) is computed as

$T_{W} = {\frac{\mu_{B,W} - \mu_{B}^{*}}{\sqrt{\sigma_{B,W}^{2} + \left( \sigma_{B}^{*} \right)^{2}}}.}$Generally, impedance index T_(W) indicates how much the impedance at thecurrent window differs from the patient's patient-specific baselineimpedance. A lower value for T_(W) generally indicates a higher fluidlevel in the patient's tissues. This example formula accounts for thevariance of the readings taken during the current window, as well as thevariance of the readings taken during the initial period.

While the example above describes one technique for establishing abaseline impedance parameter and computing an impedance index, manyother techniques are possible within the scope of the claims. Forexample, a baseline impedance parameter may be computed as a median ofseveral readings of an impedance parameter, or even from a singlereading. An impedance index may not be based on a number of standarddeviations of change from the baseline, but could be based on a simpledifference in readings from the baseline, a percentage change from thebaseline, or could be computed in any other suitable way.

In some cases, for example in a patient who has had a recent priorcardiac event, the patient's impedance readings may be changingsignificantly during the initial period during which the baseline valuesare being established. For this reason, an additional check andoptimization may be performed. In one example embodiment, the impedanceindex values are examined for the first five days of monitoring. If anywindow during the first five days has an impedance index T_(W) thatdeparts from the baseline impedance index by a significant amount, andthe patient has had a previous cardiac event within the past seven days,then the baseline values for impedance and breath volume may be reset tothe values computed for the window having the maximum departure from thebaseline impedance index.

FIG. 8 illustrates a flowchart of one exemplary method of computing abaseline breath parameter and a breath index that may be utilized inpredicting a cardiac event. A breath flag is also derived from thebreath parameter and the breath index. In some embodiments, thebreathing parameter may be monitored in a manner similar to theimpedance parameter, and a baseline established and a breath indexcomputed. Prediction of an impending cardiac event may then be based atleast in part on relationship of the ongoing measurements of the breathparameter to the patient-specific baseline breath parameter.

In step 801, the breathing parameter is monitored, for example asdescribed above, for the initial period. In step 802, a mean μ*_(V) andstandard deviation σ*_(V) are computed from the initial period breathparameter readings. The mean μ*_(V) and standard deviation σ*_(V) arepatient-specific baseline breath-related values derived from thepatient's impedance data. A different patient may have different values.

In step 803, the breath parameter is monitored for an additional timeperiod. In step 804, the data from the previous window is examined tosee if gaps exist. For example, if it is determined that any twoadjacent readings were taken more than four hours apart, it may bedetermined that the data for the current window has gaps, and the datamay not be used.

In step 805, the mean μ_(v,W) and standard deviation σ_(v,W) of thebreath parameter data from the current window are computed. In step 806,the breath index B_(W) is computed as

$B_{W} = {\frac{\mu_{V,W} - \mu_{V}^{*}}{\sqrt{\sigma_{V,W}^{2} + \left( \sigma_{V}^{*} \right)^{2}}}.}$Generally, breath index B_(W) indicates how much the breath parameter atthe current window differs from the patient's patient-specific baselinebreath parameter. A lower value for B_(W) generally indicates shallowerbreathing.

It will be recognized that additional filtering steps may be utilized inthe computation of breath index B_(W).

The relationship of the breath index B_(W) and the baseline breathparameter value may be tracked to generate a predictive breath flag. Forexample, a threshold deviation from the baseline may be established, andthe breath flag raised when the breath index exceeds the threshold. Thethreshold may be set at any suitable predictive value, for example,−0.2, −0.3, −0.4, −0.5, or some other level. The actual magnitude of thethreshold is dependent on the particular technique used to compute thebreath parameter. For example, if a variance of the baseline readings isused rather than a standard deviation, or if a different breathparameter is used, the threshold may be considerably different.

While the example above describes one technique for establishing abaseline breath parameter and computing a breath index, many othertechniques are possible within the scope of the claims. For example, abaseline breath parameter may be computed as a median of severalreadings of a breath parameter, or even from a single reading. A breathindex may not be based on a number of standard deviations of change fromthe baseline, but could be based on a simple difference in readings fromthe baseline, a percentage change from the baseline, or could becomputed in any other suitable way. In other embodiments, the breathparameter may be related to the patient's breathing rate, rather thanvolume, and the breath index may indicate a change in the patient'sbreathing rate as compared with a baseline breathing rate.

FIG. 9 illustrates a method of computing another index that may beutilized in predicting a cardiac event according to embodiments. In themethod of FIG. 9, a patient-specific predicted impedance value iscomputed, and the ongoing measurements of the impedance parametercompared with this predicted value to generate another index that may beutilized in the prediction of an impending cardiac event. In one exampleembodiment, the predicted impedance is computed from the patient's bodymass index (BMI), which is in turn computed from the patient's heightand weight.

Based on the data shown in FIG. 4, a best-fit linear model indicatesthat in general, the higher a patient's BMI, the higher the patient'spredicted impedance. The linear model includes a slope m and anintercept value b, and predicts BioZ from BMI according to the relationPredicted Impedance=b+m*BMI.Depending on the particular population of subjects used to generate thelinear model and the fitting technique used, the slope may be between1.5 and 2.0, and the intercept may be between 3 and 4. For example, theslope m may be approximately 1.5, 1.6, 1.7, 1.8, 1.9, or 2.0, and theintercept value b may be approximately 3.0, 3.2, 3.4, 3.6, 3.8, or 4.0.

FIG. 9 illustrates a flowchart for computing a ratio index from theongoing measurements of the patient's impedance and the patient'spredicted impedance. In step 901, the patient's predicted impedanceIM_(P) is computed, for example as described above. In step 902, thepatient's impedance is measured for an initial period. In step 903, thepatient's impedance is monitored for an additional time period. In step904, the impedance data for the current window is examined to see ifthere are data gaps. If the impedance parameter data is sufficient, itis filtered in step 905. It will be recognized that if the ratio indexand the impedance index described above are both to be calculated, thatsome steps such as filtering steps 706 and 905 are redundant, and needbe performed only once. In step 906, the median M_(W) of the impedanceparameter readings in the current window is determined. In step 907, theratio index R_(W) is computed as

$R_{W} = {\frac{M_{W} - {IM}_{P}}{{IM}_{P}}.}$Generally, ratio index R_(W) indicates how much the patient's currentimpedance parameter differs from the impedance that would be expectedfor a patient having similar characteristics, such as body mass index.

The ratio index R_(W) may be tracked to generate a predictive ratioflag. For example, a threshold ratio may be established, and the ratioflag raised when the ratio index exceeds the threshold. The thresholdmay be set at any suitably predictive level, for example −0.1, −0.2,−0.3, or another value. The actual magnitude of the ratio will depend onthe particular method used for computing the predicted impedance and theratio index.

While the example above describes one technique for establishing apredicted impedance computing a ratio index, many other techniques arepossible within the scope of the claims. For example, a predictedimpedance may be based on some characteristic of the patient other thanBMI, for example a body fat percentage, the patient's weight alone, orsome other characteristic or combination of characteristics. A ratioindex may be computed using some other technique, rather than a simpledifference between the current impedance and the predicted impedance.

In embodiments, any one, any combination, or all of the impedance flag,the breath flag, and the ratio flag may be used to predict events suchas impending acute decompensate heart failure.

In some embodiments, a flag is used for prediction only if it has beenraised for at least a specified duration threshold. For example, theduration threshold for the impedance flag may be set, and the impedanceflag may be considered as present for prediction only if it has beenraised continuously for at least the duration threshold. Similarduration thresholds may be set for the breath flag and the ratio flag.The duration thresholds may be set at any suitably predictive values,for example, 1 day, 2 days, 3 days, 4 days, or some other value. Theduration thresholds need not all be the same.

The flags may be used or combined in various ways. For example, theimpedance flag could be used alone to predict acute decompensated heartfailure, with a prediction output generated if the impedance flag hasbeen raised for more than its duration threshold.

However, the accuracy of prediction may be enhanced if multiplevariables are considered in the prediction algorithm. In other exampleembodiments, any two of the impedance flag, the breath flag, and theratio flag may be combined to generate the prediction output.

In a preferred embodiment, all three of the impedance flag, the breathflag, and the ratio flag are utilized in determining the predictionoutput, and the flags may be combined as follows:

-   -   1) if the impedance flag and the ratio flag are present        concurrently, and the breath flag is present for at least some        of the time that the impedance flag and the ratio flag are        concurrently present, the prediction output is generated to        predict impending acute decompensated heart failure;    -   2) if the impedance flag is not present, the prediction output        is not generated;    -   3) if the ratio flag is not available (for example, the        patient's body mass index is not known), or if the patient's        body mass index is very high, the ratio flag is considered to be        always present, and the prediction output is generated based on        the impedance flag and the breath flag as described in 1) above;    -   4) if the breath flag is not available, the breath flag is        considered to be always present, and the prediction output is        generated based on the impedance flag and the ratio flag; and    -   5) if the ratio flag and the breath flag are not available, then        the prediction output is generated based only on the impedance        flag.

As described above, the prediction method operates during the monitoringperiod, after the baseline values are established. In some cases, it maybe determined during the initial monitoring period that an acutedecompensated heart failure is likely imminent, and the predictionoutput may be generated before the baseline values are established. Forexample, once the patient's predicted impedance IM_(P) is known and areading of the patient's actual impedance parameter is available, theprediction output may be generated if the actual impedance parameterreading is below a specified minimum value. The specified minimum valuemay be set at any suitable value, for example 70% of the patient'spredicted impedance IM_(P), 75%, 80%, 85%, or some other value.

FIG. 10 graphically illustrates the event prediction logic according toembodiments. The sequence depicted in FIG. 10 may be performed for eachwindow of data gathering. In test 1001, it is determined whether patientdescriptive data such as the patient's height, weight, or body massindex is available. If so, the patient's body mass index is checked attest 1002 to see if it is greater than a cutoff value. The cutoff valuemay be set at any suitably predictive value, for example, 34 kg/m², 36kg/m², 38 kg/m², 40 kg/m², or another value. If the patient descriptivedata is not available or the patient's body mass index is too high,control passes to test 1003, where the patient's impedance index T_(W)is tested against the patient's patient-specific impedance changethreshold. If the impedance index has not gone below the threshold, thenno event prediction output will be generated. Similarly, the patient'sbreath index B_(W) is tested in test 1004 against the patient'sthreshold. If the breath index has not reached the threshold, no eventprediction output is generated. Only if both the impedance index and thebreath index have gone below their thresholds and impedance and breathflags generated, the flags are checked at test 1005 to see if the flagshave been raised for the amounts of time required for event prediction.If not, no event prediction output is generated. But if the durationlogic of test 1005 indicates that the flags have been raised forsufficient times, then an event prediction output is generated at 1006.

If tests 1001 and 1002 reveal that patient descriptive data is availableand the patient's body mass index is below the cutoff value, thenduration logic at test 1007 checks to see if the current reading iswithin the initial baseline monitoring period. If so, and if theimpedance parameter (BIOZ) is too low, as determined at test 1007, thenan event prediction output is generated at 1006. If test 1007 revealsthat the initial baseline monitoring period has passed, then controlpasses to tests 1009, 1010, and 1011, where it is determined whether allof the impedance index T_(W), the ratio index R_(W), and the breathindex B_(W) have crossed their respective thresholds so that theimpedance flag, the ratio flag, and the breath flag have been raised. Ifany of the flags has not been raised at some point, then no eventprediction output is generated. If all of the flags have been raised atleast once, then duration logic at tests 1012 tests whether the flagshave been raised for sufficient duration and in the correct relationshipfor event prediction. If not, no event prediction output is generated,but if so, the event prediction output is generated at 1006. Other waysof combining patient information to generate an event prediction outputmay be envisioned within the scope of the claims.

Experimental Clinical Studies

An experimental clinical study can be conducted on an empirical numberof patients to determine empirically parameters of the above describedadherent device and processor system so as to adjust impedance based ondata of the patient. The empirically determined parameters can be usedwith programs of the processor system to determine status of thepatient, for example to determine deterioration in the status, based onthe teachings as described herein.

Body Mass Index Study

FIG. 4 shows a scatter plot of impedance and body mass index measuredwith an experimental study comprising a measurement population ofapproximately 200 patients. The study was conducted with an adherentpatch device as described above. The data show a correspondence of bodymass index with measured impedance, and the correlation has beendetermined to be statistically significant with a p value less than0.001. The impedance increased at least about 1 Ohm per BMI unitincreased and was shown to be about 2 Ohms per unit BMI increase. Thesepatient population data indicate that an “optimal” bioimpedance value isabout 70 Ohms for a patient with a BMI of about 35 and about 50 Ohms fora patient with a BMI of about 25.

Determination and Validation of Prediction Method

In another study, 543 heart failure patients were enrolled. Each of thepatients was in New York Heart Association functional class III or IV,had an ejection fraction of 40% or less, and had a recent admission to ahospital for treatment of heart failure. The patients were monitored for90 days using a multi-sensor system that monitored bioimpedance andother data. Of the 543 patients, 206 were assigned to a developmentcohort, and data from this cohort were used to develop a predictionmethod as described above. The method was then applied to data fromother 337 patients, to validate the prediction accuracy of the method.Of the 543 patients, 314 completed the study.

Both single-variable and multi-variable prediction methods wereevaluated, with the following results achieved in the validation patientpopulation:

False Sensi- Speci- Positive tivity ficity Rate (Event/ AdvanceDetection (%) (%) patient-yr.) Time (Days) Impedance Index 83 40 9.4Impedance Index 70 77 2.3 and Breath Index Impedance Index 71 74 3.2 andPredicted Impedance Impedance Index, 65 90 0.7 Mean 11.4 +/− 9.0 BreathIndex, and Median 9.0 Predicted Range 2.4 to 33.3 Impedance

Sensitivity is a measure of the method's ability to recognize impendingevents, and may be defined as the number of true positive predictionsdivided by the number of cardiac events that occurred in the patientpopulation, that isSensitivity=#TP/#Events.

Specificity is a measure of the method's ability to correctly recognizethat no cardiac event is pending, and may be defined as the number oftrue negative readings divided by the number of patient records withoutevents, that isSpecificity=#TN/#Patient records without events.

The false positive (FP) rate is a measure of the method's tendency topredict a cardiac event when none actually occurs, and may be defined asthe number of false positive predictions divided by the total dataduration in years, that isFP Rate=#FP/Total data duration.

A true positive may be defined as a prediction output that culminates ina cardiac event, for example a prediction output lasting at least 24hours and culminating in an acute decompensated heart failure event.

A false positive may be defined as the generation of a prediction outputwithout the predicted event occurring. A prediction output that switchesoff and is not followed by an event may also be considered a falsepositive.

A false negative may be defined as a cardiac even that is not precededby a prediction output within the preceding 24 hours.

A true negative occurs when no prediction output is generated, and nocardiac events occur during the monitoring period.

As can bee seen from the experimental results, multi-parameterprediction results in significantly improved prediction accuracy, ascompared with single-parameter prediction, especially in the reductionof the false positive rate.

The above data are exemplary and a person or ordinary skill in the artwill recognize many variations and alterations based on the teachingsdescribed herein.

While the exemplary embodiments have been described in some detail, byway of example and for clarity of understanding, those of skill in theart will recognize that a variety of modifications, adaptations, andchanges may be employed. Hence, the scope of the present inventionshould be limited solely by the appended claims.

What is claimed is:
 1. A system for predicting a cardiac event of apatient, the system comprising: an adherent device attachable to apatient to electronically measure impedance data from the patient; aninput mechanism for receiving patient descriptive data indicating atleast one descriptive characteristic of the patient; and a processor anda tangible memory readable by the processor; wherein the memory storesinstructions that, when executed by the processor, cause the system to:receive the impedance data; establish at least one personalized valuefor the patient based on the impedance data; receive patient descriptivedata indicating at least one descriptive characteristic of the patient;establish a predicted impedance specific to the patient, based at leastin part on the patient descriptive data; and generate, based at least inpart on the impedance data, the predicted impedance, and the at leastone personalized value, a patient event prediction output predictive ofa patient cardiac event.
 2. The system of claim 1, wherein the adherentdevice further comprises: at least two electrodes in contact with thepatient's skin; and an electronics module that measures the impedancebetween at least two of the electrodes and transmits the impedance datarepresenting the impedance to the processor.
 3. The system of claim 2,wherein the adherent device comprises the processor.
 4. The system ofclaim 1, further comprising: a computer system that comprises theprocessor; and a communication link over which the impedance data istransmitted from the adherent device to the computer system.
 5. Thesystem of claim 4, further comprising an intermediate device thatreceives the impedance data from the adherent device and relays theimpedance data to the computer system.
 6. The system of claim 4, whereinthe adherent device transmits the impedance data to the computer systemat least in part via a wireless link.
 7. The system of claim 1, whereinthe patient event prediction output is predictive of an acutedecompensated heart failure event.
 8. The system of claim 1, wherein theinstructions, when executed by the processor, further cause the systemto: establish from the impedance data a baseline impedance specific tothe patient; and generate the patient event prediction output based atleast on part on the relationship of ongoing impedance measurements tothe baseline impedance.
 9. The system of claim 1, wherein theinstructions, when executed by the processor, further cause the systemto: establish from the impedance data a baseline breath parameterspecific to the patient; and generate the patient event predictionoutput based at least on part on the relationship of ongoingmeasurements of the breath parameter to the baseline breath parameter.10. The system of claim 1, wherein the instructions, when executed bythe processor, further cause the system to: generate the patient eventprediction output based at least on part on the relationship of ongoingmeasurements of the impedance to the patient's predicted impedance. 11.The system of claim 1, wherein the instructions, when executed by theprocessor, further cause the system to: establish from the impedancedata a baseline impedance specific to the patient; establish from theimpedance data a baseline breath parameter specific to the patient; andgenerate the patient event prediction output based at least on part onthe relationship of ongoing measurements of the impedance to thebaseline impedance, and the relationship of ongoing measurements of thebreath parameter to the baseline breath parameter.
 12. The system ofclaim 11, wherein the instructions, when executed by the processor,further cause the system to: generate the patient event predictionoutput based at least in part on the relationship of ongoingmeasurements of the impedance to the patient's predicted impedance. 13.The system of claim 12, wherein the instructions, when executed by theprocessor, further cause the system to: compute an impedance indexindicative of a change in the impedance relative to the baselineimpedance over time; compute a breath index indicative of a change inthe breath parameter relative to the baseline breath parameter overtime; compute a predicted impedance index indicative of a change in theimpedance relative to the predicted impedance over time; and compare theimpedance index, the breath index, and the predicted impedance indexwith respective preselected thresholds.
 14. The system of claim 13,wherein the instructions, when executed by the processor, further causethe system to: compute an amount of time for which at least one of theindices has exceeded its respective threshold.
 15. The method of claim14, wherein the instructions, when executed by the processor, furthercause the system to generate the patient prediction output predicting acardiac event when the impedance index and the predicted impedance indexhave both exceeded their respective thresholds for time periods ofpreselected duration, and the breath index has exceeded its respectivethreshold at least once during the time period.
 16. The system of claim1, wherein the patient descriptive data indicate a body composition ofthe patient, and wherein the instructions, when executed by theprocessor, cause the system to establish the predicted impedance basedat least in part on the indicated body composition of the patient. 17.The system of claim 1, wherein the patient descriptive data comprises aheight and weight of the patient, and wherein the instructions, whenexecuted by the processor, cause the system to establish the predictedimpedance based at least in part on the height and weight of thepatient.
 18. The system of claim 1, wherein the instructions, whenexecuted by the processor, cause the system to establish the predictedimpedance based at least in part on a body mass index of the patient.19. A system for predicting a cardiac event of a patient, the systemcomprising; an adherent device attachable to a patient to electronicallymeasure impedance data from the patient; an input mechanism forreceiving patient descriptive data indicating at least one descriptivecharacteristic of the patient; and a processor and a tangible memoryreadable by the processor; wherein the memory stores instructions that,when executed by the processor, cause the system to: receive theimpedance data; receive patient descriptive data indicating at least onedescriptive characteristic of the patient: establish a predictedimpedance specific to the patient, based at least in part on the patientdescriptive data; establish from the impedance data a baseline impedancespecific to the patient; compute an impedance index indicative of achange in the impedance relative to the baseline impedance over time;compare the impedance index with a preselected impedance indexthreshold; compute an amount of time for which the impedance index hasexceeded the impedance index threshold; and generate, based at least inpart on the impedance index and the predicted impedance, a patient eventprediction output predictive of a patient cardiac event, only if theimpedance index has exceeded the impedance index threshold for at leasta preselected time.